Automated systems are known that use background subtraction (BGS) methods to distinguish foreground objects from a determined image background as a function of analysis results from motion inference algorithms. In some examples, adaptive background modeling is used to detect foreground masks obtained with respect to a BGS model. BGS systems may also use adaptive mixtures of Gaussian models to detect non-static objects as moving foreground objects distinct from other objects or scene image data within the background model of the image scene.
Accurately distinguishing between static and non-static objects in prior art BGS systems is problematic. Non-static objects that remain motionless for a given period of time may be erroneously treated as static objects and learned into a background scene model. Healing problems may arise when formerly stationary objects begin to move, wherein the objects remain in the foreground as “ghosts” after they have in fact moved on and out of the image scene. Noisy light and shadow data within the analyzed video image may present still further problems in object detection and tracking, wherein current frame image data may change suddenly due to quickly changing lighting conditions and thereby cause false moving object detection events.